| Peer-Reviewed

Approximation of the Cut Function by Some Generic Logistic Functions and Applications

Received: 17 August 2016    Accepted: 27 August 2016    Published: 12 September 2016
Views:       Downloads:
Abstract

In this paper we study the uniform approximation of the cut function by smooth sigmoid functions such as Nelder and Turner–Blumenstein–Sebaugh growth functions. To illustrate the use of one of the models we have fitted the model to the “classical Verhulst data”. Several numerical examples are presented throughout the paper using the contemporary computer algebra system MATHEMATICA.

Published in Advances in Applied Sciences (Volume 1, Issue 2)
DOI 10.11648/j.aas.20160102.11
Page(s) 24-29
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Sigmoid Functions, Cut Function, Step Function, Nelder Growth Function,Turner–Blumenstein–Sebaugh Generic Function, Uniform Approximation

References
[1] S. Shoffner and S. Schnell, Estimation of the lag time in a subsequent monomer addition model for fibril elongation, bioRxiv The preprint server for biology, 2015, pp. 1–8, doi: 10.1101/034900.
[2] P. Arosio, T. P. J. Knowles, and S. Linse, On the lag phase in amyloid fibril formation, Physical Chemistry Chemical Physics, vol. 17, 2015, pp. 7606–7618, doi: 10.1039/C4CP05563B.
[3] N. Kyurkchiev, A note on the new geometric representation for the parameters in the fibril elongation process, Compt. rend. Acad. bulg. Sci., vol. 69 (8), 2016, pp. 963–972.
[4] S. Markov, Building reaction kinetic models for amyloid fibril growth, BIOMATH, vol. 5, 2016, http://dx.dpi.org/10.11145/j.biomath.2016.07.311.
[5] M. Turner, B. Blumenstein, and J. Sebaugh, A Generalization of the Logistic Law of Growth, Biometrics, vol. 25 (3), 1969, pp. 577–580.
[6] J. A. Nelder, The fitting of a generalization of the logistic curve, Biometrics, vol. 17, 1961, pp. 89–110.
[7] P. F. Verhulst, Notice sur la loi que la population poursuit dans son accroissement, Correspondance mathematique et physique, vol. 10, 1838, pp. 113–121.
[8] N. Kyurkchiev and S. Markov, On the Hausdorff distance between the Heaviside step function and Verhulst logistic function, J. Math. Chem., vol. 54 (1), 2016, pp. 109–119, doi: 10.1007/S10910-015-0552-0.
[9] N. Kyurkchiev and S. Markov, Sigmoidal functions: some computational and modelling aspects, Biomath Communications, vol. 1 (2), 2014, pp. 30–48, doi: 10.11145/j.bmc.2015.03.081.
[10] A. Iliev, N. Kyurkchiev and S. Markov, On the approximation of the cut and step functions by logistic and Gompertz functions, Biomath, vol. 4 (2), 2015, pp. 2–13.
[11] N. Kyurkchiev and S. Markov, On the approximation of the generalized cut function of degree by smooth sigmoid functions, Serdica J. Computing, vol. 9 (1), 2015, pp. 101–112.
[12] A. Iliev, N. Kyurkchiev, and S. Markov, On the Approximation of the step function by some sigmoid functions, Mathematics and Computers in Simulation, 2015, doi: 10.1016/j.matcom.2015.11.005.
[13] N. Kyurkchiev and A. Iliev, On some growth curve modeling: approximation theory and applications, Int. J. of Trends in Research and Development, vol. 3 (3), 2016, pp. 466–471, http://www.ijtrd.com/papers/IJTRD3869.pdf
[14] N. Kyurkchiev and S. Markov, Sigmoid functions: Some Approximation and Modelling Aspects, LAP LAMBERT Academic Publishing, Saarbrucken, 2015, ISBN 978-3-659-76045-7.
[15] N. Kyurkchiev and A. Iliev, A note on some growth curves arising from Box-Cox transformation, Int. J. of Engineering Works, vol. 3 (6), 2016, pp. 47–51, ISSN: 2409-2770.
[16] N. Kyurkchiev, S. Markov, and A. Iliev, A note on the Schnute growth model, Int. J. of Engineering Research and Development, vol. 12 (6), 2016, pp. 47–54, ISSN: 2278-067X, http://www.ijerd.com/paper/vol12-issue6/Verison-1/G12614754.pdf
[17] A. Iliev, N. Kyurkchiev, and S. Markov, On the Hausdorff distance between the shifted Heaviside step function and the transmuted Stannard growth function, BIOMATH, 2016, (accepted).
[18] N. Kyurkchiev, On the Approximation of the step function by some cumulative distribution functions, Compt. rend. Acad. bulg. Sci., vol. 68 (12), 2015, pp. 1475–1482.
[19] V. Kyurkchiev and N. Kyurkchiev, On the Approximation of the Step function by Raised-Cosine and Laplace Cumulative Distribution Functions, European International Journal of Science and Technology, vol. 4 (9), 2016, pp. 75–84.
[20] D. Costarelli and R. Spigler, Approximation results for neural network operators activated by sigmoidal functions, Neural Networks, vol. 44, 2013, pp. 101–106.
[21] D. Costarelli and G. Vinti, Pointwise and uniform approximation by multivariate neural network operators of the max-product type, Neural Networks, 2016, doi: 10.1016/j.neunet.2016.06.002.
[22] Costarelli, D., R. Spigler, Solving numerically nonlinear systems of balance laws by multivariate sigmoidal functions approximation, Computational and Applied Mathematics, 2016, doi: 10.1007/s40314-016-0334-8.
[23] D. Costarelli and G. Vinti, Convergence for a family of neural network operators in Orlicz spaces, Mathematische Nachrichten, 2016, doi: 10.1002/mana.20160006.
[24] N. Guliyev and V. Ismailov, A single hidden layer feedforward network with only one neuron in the hidden layer san approximate any univariate function, Neural Computation, vol. 28, 2016, pp. 1289–1304.
[25] J. Dombi and Z. Gera, The Approximation of Piecewise Linear Membership Functions and Lukasiewicz Operators, Fuzzy Sets and Systems, vol. 154 (2), 2005, pp. 275–286.
Cite This Article
  • APA Style

    Nikolay Kyurkchiev, Svetoslav Markov. (2016). Approximation of the Cut Function by Some Generic Logistic Functions and Applications. Advances in Applied Sciences, 1(2), 24-29. https://doi.org/10.11648/j.aas.20160102.11

    Copy | Download

    ACS Style

    Nikolay Kyurkchiev; Svetoslav Markov. Approximation of the Cut Function by Some Generic Logistic Functions and Applications. Adv. Appl. Sci. 2016, 1(2), 24-29. doi: 10.11648/j.aas.20160102.11

    Copy | Download

    AMA Style

    Nikolay Kyurkchiev, Svetoslav Markov. Approximation of the Cut Function by Some Generic Logistic Functions and Applications. Adv Appl Sci. 2016;1(2):24-29. doi: 10.11648/j.aas.20160102.11

    Copy | Download

  • @article{10.11648/j.aas.20160102.11,
      author = {Nikolay Kyurkchiev and Svetoslav Markov},
      title = {Approximation of the Cut Function by Some Generic Logistic Functions and Applications},
      journal = {Advances in Applied Sciences},
      volume = {1},
      number = {2},
      pages = {24-29},
      doi = {10.11648/j.aas.20160102.11},
      url = {https://doi.org/10.11648/j.aas.20160102.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.aas.20160102.11},
      abstract = {In this paper we study the uniform approximation of the cut function by smooth sigmoid functions such as Nelder and Turner–Blumenstein–Sebaugh growth functions. To illustrate the use of one of the models we have fitted the model to the “classical Verhulst data”. Several numerical examples are presented throughout the paper using the contemporary computer algebra system MATHEMATICA.},
     year = {2016}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Approximation of the Cut Function by Some Generic Logistic Functions and Applications
    AU  - Nikolay Kyurkchiev
    AU  - Svetoslav Markov
    Y1  - 2016/09/12
    PY  - 2016
    N1  - https://doi.org/10.11648/j.aas.20160102.11
    DO  - 10.11648/j.aas.20160102.11
    T2  - Advances in Applied Sciences
    JF  - Advances in Applied Sciences
    JO  - Advances in Applied Sciences
    SP  - 24
    EP  - 29
    PB  - Science Publishing Group
    SN  - 2575-1514
    UR  - https://doi.org/10.11648/j.aas.20160102.11
    AB  - In this paper we study the uniform approximation of the cut function by smooth sigmoid functions such as Nelder and Turner–Blumenstein–Sebaugh growth functions. To illustrate the use of one of the models we have fitted the model to the “classical Verhulst data”. Several numerical examples are presented throughout the paper using the contemporary computer algebra system MATHEMATICA.
    VL  - 1
    IS  - 2
    ER  - 

    Copy | Download

Author Information
  • Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

  • Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Sofia, Bulgaria

  • Sections